





import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Dict:




				"""simple docstring"""






				snake_case   :					int    						=				args.log_outputs
				snake_case   :					Union[str, Any]    						=				'''_'''.join(args.dataset.split('''/'''   ) + [args.config, args.split]   )

				# load metric
				snake_case   :					Tuple    						=				load_metric('''wer'''   )
				snake_case   :					Optional[int]    						=				load_metric('''cer'''   )

				# compute metrics
				snake_case   :					str    						=				wer.compute(references=result['''target''']		,     predictions=result['''prediction''']   )
				snake_case   :					Union[str, Any]    						=				cer.compute(references=result['''target''']		,     predictions=result['''prediction''']   )

				# print & log results
				snake_case   :					Tuple    						=				F"WER: {wer_result}\nCER: {cer_result}"
				print(__magic_name__   )

				with open(F"{dataset_id}_eval_results.txt"		,     '''w'''   ) as f:
								f.write(__magic_name__   )

				# log all results in text file. Possibly interesting for analysis
				if log_outputs is not None:
								snake_case   :					Dict    						=				F"log_{dataset_id}_predictions.txt"
								snake_case   :					List[str]    						=				F"log_{dataset_id}_targets.txt"

								with open(__magic_name__		,     '''w'''   ) as p, open(__magic_name__		,     '''w'''   ) as t:
												# mapping function to write output
												def write_to_file(__magic_name__		,     __magic_name__   ):
																p.write(F"{i}" + '''\n'''   )
																p.write(batch['''prediction'''] + '''\n'''   )
																t.write(F"{i}" + '''\n'''   )
																t.write(batch['''target'''] + '''\n'''   )

												result.map(__magic_name__		,     with_indices=__magic_name__   )
def 							a_	(    __magic_name__   )				->			List[str]:




				"""simple docstring"""






				snake_case   :					Tuple    						=				'''[,?.!\-\;\:\"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				snake_case   :					int    						=				re.sub(__magic_name__		,     ''''''		,     text.lower()   )

				# In addition, we can normalize the target text, e.g. removing new lines characters etc...
				# note that order is important here!
				snake_case   :					List[str]    						=				['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								snake_case   :					Optional[int]    						=				''' '''.join(text.split(__magic_name__   )   )

				return text
def 							a_	(    __magic_name__   )				->			str:




				"""simple docstring"""






				snake_case   :					Optional[Any]    						=				load_dataset(args.dataset		,     args.config		,     split=args.split		,     use_auth_token=__magic_name__   )

				# for testing: only process the first two examples as a test
				# dataset = dataset.select(range(10))

				# load processor
				snake_case   :					str    						=				AutoFeatureExtractor.from_pretrained(args.model_id   )
				snake_case   :					List[Any]    						=				feature_extractor.sampling_rate

				# resample audio
				snake_case   :					List[Any]    						=				dataset.cast_column('''audio'''		,     Audio(sampling_rate=__magic_name__   )   )

				# load eval pipeline
				if args.device is None:
								snake_case   :					int    						=				0 if torch.cuda.is_available() else -1
				snake_case   :					Tuple    						=				pipeline('''automatic-speech-recognition'''		,     model=args.model_id		,     device=args.device   )

				# map function to decode audio
				def map_to_pred(__magic_name__   ):
								snake_case   :					Union[str, Any]    						=				asr(
								    batch['''audio''']['''array''']		,     chunk_length_s=args.chunk_length_s		,     stride_length_s=args.stride_length_s   )

								snake_case   :					int    						=				prediction['''text''']
								snake_case   :					Tuple    						=				normalize_text(batch['''sentence''']   )
								return batch

				# run inference on all examples
				snake_case   :					int    						=				dataset.map(__magic_name__		,     remove_columns=dataset.column_names   )

				# compute and log_results
				# do not change function below
				log_results(__magic_name__		,     __magic_name__   )


if __name__ == "__main__":
			_a     :    List[Any]         =						argparse.ArgumentParser()

			parser.add_argument(
			    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
			)
			parser.add_argument(
			    '--dataset',
			    type=str,
			    required=True,
			    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
			)
			parser.add_argument(
			    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
			)
			parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
			parser.add_argument(
			    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
			)
			parser.add_argument(
			    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
			)
			parser.add_argument(
			    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
			)
			parser.add_argument(
			    '--device',
			    type=int,
			    default=None,
			    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
			)
			_a     :    Optional[int]         =						parser.parse_args()

			main(args)







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'fnet'





      def __init__(			self ,		lowercase=32_000 ,		lowercase=768 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu_new" ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=4 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=512 ,		lowercase=3 ,		lowercase=1 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			use_tpu_fourier_optimizations
            lowerCAmelCase    		=			tpu_short_seq_length






